{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "\n",
    "from sklearn.preprocessing import Imputer,Normalizer,scale\n",
    "from sklearn.cross_validation import train_test_split, StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize'] = 8,6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_histograms( df , variables , n_rows , n_cols ):\n",
    "    fig = plt.figure( figsize = ( 16 , 12 ) )\n",
    "    for i, var_name in enumerate( variables ):\n",
    "        ax=fig.add_subplot( n_rows , n_cols , i+1 )\n",
    "        df[ var_name ].hist( bins=10 , ax=ax )\n",
    "        ax.set_title( 'Skew: ' + str( round( float( df[ var_name ].skew() ) , ) ) ) # + ' ' + var_name ) #var_name+\" Distribution\")\n",
    "        ax.set_xticklabels( [] , visible=False )\n",
    "        ax.set_yticklabels( [] , visible=False )\n",
    "    fig.tight_layout()  # Improves appearance a bit.\n",
    "    plt.show()\n",
    "\n",
    "def plot_distribution( df , var , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , hue=target , aspect=4 , row = row , col = col )\n",
    "    facet.map( sns.kdeplot , var , shade= True )\n",
    "    facet.set( xlim=( 0 , df[ var ].max() ) )\n",
    "    facet.add_legend()\n",
    "\n",
    "def plot_categories( df , cat , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , row = row , col = col )\n",
    "    facet.map( sns.barplot , cat , target )\n",
    "    facet.add_legend()\n",
    "def plot_correlation_map( df ):\n",
    "    corr = titanic.corr()\n",
    "    _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
    "    cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "    _ = sns.heatmap(\n",
    "        corr, \n",
    "        cmap = cmap,\n",
    "        square=True, \n",
    "        cbar_kws={ 'shrink' : .9 }, \n",
    "        ax=ax, \n",
    "        annot = True, \n",
    "        annot_kws = { 'fontsize' : 12 }\n",
    "    )\n",
    "\n",
    "def describe_more( df ):\n",
    "    var = [] ; l = [] ; t = []\n",
    "    for x in df:\n",
    "        var.append( x )\n",
    "        l.append( len( pd.value_counts( df[ x ] ) ) )\n",
    "        t.append( df[ x ].dtypes )\n",
    "    levels = pd.DataFrame( { 'Variable' : var , 'Levels' : l , 'Datatype' : t } )\n",
    "    levels.sort_values( by = 'Levels' , inplace = True )\n",
    "    return levels\n",
    "\n",
    "def plot_variable_importance( X , y ):\n",
    "    tree = DecisionTreeClassifier( random_state = 99 )\n",
    "    tree.fit( X , y )\n",
    "    plot_model_var_imp( tree , X , y )\n",
    "    \n",
    "def plot_model_var_imp( model , X , y ):\n",
    "    imp = pd.DataFrame( \n",
    "        model.feature_importances_  , \n",
    "        columns = [ 'Importance' ] , \n",
    "        index = X.columns \n",
    "    )\n",
    "    imp = imp.sort_values( [ 'Importance' ] , ascending = True )\n",
    "    imp[ : 10 ].plot( kind = 'barh' )\n",
    "    print (model.score( X , y ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets: full: (1309, 12) titanic: (891, 12)\n"
     ]
    }
   ],
   "source": [
    "# get titanic & test csv files as a DataFrame\n",
    "train = pd.read_csv(\"input/train.csv\")\n",
    "test    = pd.read_csv(\"input/test.csv\")\n",
    "\n",
    "full = train.append( test , ignore_index = True )\n",
    "titanic = full[ :891 ]\n",
    "\n",
    "del train , test\n",
    "\n",
    "print ('Datasets:' , 'full:' , full.shape , 'titanic:' , titanic.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  \n",
       "3       1  female      1       1.0            113803  \n",
       "4       3    male      0       0.0            373450  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the code to see the variables, then read the variable description below to understand them.\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare       Parch  PassengerId      Pclass  \\\n",
       "count  714.000000  891.000000  891.000000   891.000000  891.000000   \n",
       "mean    29.699118   32.204208    0.381594   446.000000    2.308642   \n",
       "std     14.526497   49.693429    0.806057   257.353842    0.836071   \n",
       "min      0.420000    0.000000    0.000000     1.000000    1.000000   \n",
       "25%           NaN    7.910400    0.000000   223.500000    2.000000   \n",
       "50%           NaN   14.454200    0.000000   446.000000    3.000000   \n",
       "75%           NaN   31.000000    0.000000   668.500000    3.000000   \n",
       "max     80.000000  512.329200    6.000000   891.000000    3.000000   \n",
       "\n",
       "            SibSp    Survived  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.523008    0.383838  \n",
       "std      1.102743    0.486592  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      0.000000    0.000000  \n",
       "75%      1.000000    1.000000  \n",
       "max      8.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiJgbW0jc15sbO1w5p+TJbeLjICdnM+JjZ0doqOi5LZZu2IZ6jdoiFZftClxu5s3rIOa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x926ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation_map(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oh36GxYIrtRvONqkUfbacok+WQrGPouVv4/5sGTF/HUXMkFFYIiLPYMciIiIi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\nAf58jN4dZRT/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5b56b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution( titanic, var = 'Age', target = 'Survived', row = 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvffyzDPPNBtXVVXFrFmzGDduHCtWrKCgoIBrrrmG6upqAO6//35WrlzJf/7n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\nXcEVP3+R9W9XtNeUJUmSJEmSlACDvV6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sAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7bd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(titanic, var = 'Fare', target = 'Survived',  row = 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Omv38/DBnzhx89NFH+OijjzBq1Ci8/fbbGDdunL3qE72Gx1mMRiOMRiMyMjJQU1Pj6LKI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\nEYkCw4yIROH/Acv4Xl+O1MprAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa65a110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(titanic, cat = 'Embarked', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/fsvrJCLxEkWYNTQ0wM/PD3L5d/Or+Pv7w2w2o7Gx0a7vN998g0GDBtm1+fv7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\nkSQwzIhIEhhmRCQJDDMikoT/ByBYohQRwGhuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6618d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(titanic, cat = 'Sex', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NrURkUw80O5O3tzfmzp2Ljz/+GB9//DGGDBmCd999F6NHj7ZWfUREjWLxWzPqlZeXIzs7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\nIhIFhhkRiQLDjIhE4f8BcgktI+f/ZiwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa67ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(titanic, cat = 'Pclass', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zsWfPHuzZswejRo3Cn//8Z0ycOLGz6iMicojLq2a0qa+vR3FxMYqLi3HixAn4+/tzcUYi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa709630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(titanic, cat = 'SibSp', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7duzAhQsXMHbs2Fv+nLNmtCyGGJE5m8Js5MiRzV0HEdE9sSnMbp5Bg4hICGz6AODChQt3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\nkSj8H46JWdPdL8VPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa72fd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(titanic, cat = 'Parch', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sex = pd.Series( np.where( full.Sex == 'male', 1, 0), name = 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_C</th>\n",
       "      <th>Pclass_Q</th>\n",
       "      <th>Pclass_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_C  Pclass_Q  Pclass_S\n",
       "0       0.0       0.0       1.0\n",
       "1       1.0       0.0       0.0\n",
       "2       0.0       0.0       1.0\n",
       "3       0.0       0.0       1.0\n",
       "4       0.0       0.0       1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked = pd.get_dummies(full.Embarked, prefix = 'Pclass')\n",
    "embarked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_1  Pclass_2  Pclass_3\n",
       "0       0.0       0.0       1.0\n",
       "1       1.0       0.0       0.0\n",
       "2       0.0       0.0       1.0\n",
       "3       1.0       0.0       0.0\n",
       "4       0.0       0.0       1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass = pd.get_dummies(full.Pclass, prefix = 'Pclass')\n",
    "pclass.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare\n",
       "0  22.0   7.2500\n",
       "1  38.0  71.2833\n",
       "2  26.0   7.9250\n",
       "3  35.0  53.1000\n",
       "4  35.0   8.0500"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed = pd.DataFrame()\n",
    "\n",
    "imputed['Age'] = full.Age.fillna( full.Age.mean())\n",
    "\n",
    "imputed['Fare'] = full.Fare.fillna( full.Fare.mean())\n",
    "\n",
    "imputed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title = pd.DataFrame()\n",
    "title['Title'] = full['Name'].map(lambda name: name.split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Title_Dictionary = {\n",
    "    \"Capt\":       \"Officer\",\n",
    "    \"Col\":        \"Officer\",\n",
    "    \"Major\":      \"Officer\",\n",
    "    \"Jonkheer\":   \"Royalty\",\n",
    "    \"Don\":        \"Royalty\",\n",
    "                    \"Sir\" :       \"Royalty\",\n",
    "                    \"Dr\":         \"Officer\",\n",
    "                    \"Rev\":        \"Officer\",\n",
    "                    \"the Countess\":\"Royalty\",\n",
    "                    \"Dona\":       \"Royalty\",\n",
    "                    \"Mme\":        \"Mrs\",\n",
    "                    \"Mlle\":       \"Miss\",\n",
    "                    \"Ms\":         \"Mrs\",\n",
    "                    \"Mr\" :        \"Mr\",\n",
    "                    \"Mrs\" :       \"Mrs\",\n",
    "                    \"Miss\" :      \"Miss\",\n",
    "                    \"Master\" :    \"Master\",\n",
    "                    \"Lady\" :      \"Royalty\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Master  Miss   Mr  Mrs  Officer  Royalty\n",
       "0     0.0   0.0  1.0  0.0      0.0      0.0\n",
       "1     0.0   0.0  0.0  1.0      0.0      0.0\n",
       "2     0.0   1.0  0.0  0.0      0.0      0.0\n",
       "3     0.0   0.0  0.0  1.0      0.0      0.0\n",
       "4     0.0   0.0  1.0  0.0      0.0      0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title['Title'] = title.Title.map(Title_Dictionary)\n",
    "title = pd.get_dummies(title.Title)\n",
    "title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \\\n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1      0.0      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3      0.0      0.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
       "4      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   Cabin_U  \n",
       "0      1.0  \n",
       "1      0.0  \n",
       "2      1.0  \n",
       "3      0.0  \n",
       "4      1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabin = pd.DataFrame()\n",
    "\n",
    "cabin['Cabin'] = full.Cabin.fillna('U')\n",
    "cabin['Cabin'] = cabin['Cabin'].map(lambda c:c[0])\n",
    "\n",
    "cabin = pd.get_dummies( cabin['Cabin'], prefix = 'Cabin')\n",
    "\n",
    "cabin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket_A</th>\n",
       "      <th>Ticket_A4</th>\n",
       "      <th>Ticket_A5</th>\n",
       "      <th>Ticket_AQ3</th>\n",
       "      <th>Ticket_AQ4</th>\n",
       "      <th>Ticket_AS</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_CA</th>\n",
       "      <th>Ticket_CASOTON</th>\n",
       "      <th>Ticket_FC</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket_A  Ticket_A4  Ticket_A5  Ticket_AQ3  Ticket_AQ4  Ticket_AS  \\\n",
       "0       0.0        0.0        1.0         0.0         0.0        0.0   \n",
       "1       0.0        0.0        0.0         0.0         0.0        0.0   \n",
       "2       0.0        0.0        0.0         0.0         0.0        0.0   \n",
       "3       0.0        0.0        0.0         0.0         0.0        0.0   \n",
       "4       0.0        0.0        0.0         0.0         0.0        0.0   \n",
       "\n",
       "   Ticket_C  Ticket_CA  Ticket_CASOTON  Ticket_FC     ...      Ticket_SOTONO2  \\\n",
       "0       0.0        0.0             0.0        0.0     ...                 0.0   \n",
       "1       0.0        0.0             0.0        0.0     ...                 0.0   \n",
       "2       0.0        0.0             0.0        0.0     ...                 0.0   \n",
       "3       0.0        0.0             0.0        0.0     ...                 0.0   \n",
       "4       0.0        0.0             0.0        0.0     ...                 0.0   \n",
       "\n",
       "   Ticket_SOTONOQ  Ticket_SP  Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  \\\n",
       "0             0.0        0.0           0.0            0.0            0.0   \n",
       "1             0.0        0.0           0.0            0.0            0.0   \n",
       "2             0.0        0.0           0.0            1.0            0.0   \n",
       "3             0.0        0.0           0.0            0.0            0.0   \n",
       "4             0.0        0.0           0.0            0.0            0.0   \n",
       "\n",
       "   Ticket_SWPP  Ticket_WC  Ticket_WEP  Ticket_XXX  \n",
       "0          0.0        0.0         0.0         0.0  \n",
       "1          0.0        0.0         0.0         0.0  \n",
       "2          0.0        0.0         0.0         0.0  \n",
       "3          0.0        0.0         0.0         1.0  \n",
       "4          0.0        0.0         0.0         1.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cleanTicket(ticket):\n",
    "    ticket = ticket.replace('.','')\n",
    "    ticket = ticket.replace('/','')\n",
    "    ticket = ticket.split()\n",
    "    ticket = map(lambda t: t.strip(), ticket)\n",
    "    ticket = list(filter(lambda t: not t.isdigit(), ticket))\n",
    "    if len(ticket) > 0:\n",
    "        return ticket[0]\n",
    "    else:\n",
    "        return 'XXX'\n",
    "    \n",
    "ticket = pd.DataFrame()\n",
    "\n",
    "ticket['Ticket'] = full['Ticket'].map( cleanTicket)\n",
    "ticket = pd.get_dummies( ticket['Ticket'], prefix = 'Ticket')\n",
    "\n",
    "ticket.shape\n",
    "ticket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Family_Single</th>\n",
       "      <th>Family_Small</th>\n",
       "      <th>Family_Larget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Family_Single  Family_Small  Family_Larget\n",
       "0           2              0             1              0\n",
       "1           2              0             1              0\n",
       "2           1              1             0              0\n",
       "3           2              0             1              0\n",
       "4           1              1             0              0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family = pd.DataFrame()\n",
    "family['FamilySize'] = full['Parch'] + full['SibSp'] + 1\n",
    "family['Family_Single'] = family['FamilySize'].map(lambda s: 1 if s == 1 else 0)\n",
    "family['Family_Small'] = family['FamilySize'].map(lambda s: 1 if 2 <= s <= 4 else 0)\n",
    "family['Family_Larget'] = family['FamilySize'].map(lambda s: 1 if 5 <= s else 0)\n",
    "\n",
    "family.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass_C</th>\n",
       "      <th>Pclass_Q</th>\n",
       "      <th>Pclass_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Pclass_C  Pclass_Q  Pclass_S  Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0  22.0   7.2500       0.0       0.0       1.0      0.0      0.0      0.0   \n",
       "1  38.0  71.2833       1.0       0.0       0.0      0.0      0.0      1.0   \n",
       "2  26.0   7.9250       0.0       0.0       1.0      0.0      0.0      0.0   \n",
       "3  35.0  53.1000       0.0       0.0       1.0      0.0      0.0      1.0   \n",
       "4  35.0   8.0500       0.0       0.0       1.0      0.0      0.0      0.0   \n",
       "\n",
       "   Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  Sex  \n",
       "0      0.0      0.0      0.0      0.0      0.0      1.0    1  \n",
       "1      0.0      0.0      0.0      0.0      0.0      0.0    0  \n",
       "2      0.0      0.0      0.0      0.0      0.0      1.0    0  \n",
       "3      0.0      0.0      0.0      0.0      0.0      0.0    0  \n",
       "4      0.0      0.0      0.0      0.0      0.0      1.0    1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_X = pd.concat([imputed, embarked, cabin, sex], axis = 1)\n",
    "full_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 15) (623, 15) (268, 15) (623,) (268,) (418, 15)\n"
     ]
    }
   ],
   "source": [
    "train_valid_X = full_X[0:891]\n",
    "train_valid_y = titanic.Survived\n",
    "test_X = full_X[891: ]\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(train_valid_X,train_valid_y, train_size = .7)\n",
    "\n",
    "print(full_X.shape, train_X.shape, valid_X.shape, train_y.shape, valid_y.shape, test_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.987158908507\n"
     ]
    },
    {
     "data": {
      "image/png": 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A8DG0ggAAAAAGEKwBAAAAAwjWAAAAgAHGgrXb7VZ2drYcDoemTJmi2bNnq7CwUB6P51O/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\nEREREZEM2FgTEREREcng/wCRlSUNit2IowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaaa7290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_variable_importance(train_X,train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.987158908507\n"
     ]
    },
    {
     "data": {
      "image/png": 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A8DG0ggAAAAAGEKwBAAAAAwjWAAAAgAHGgrXb7VZ2drYcDoemTJmi2bNnq7CwUB6P51O/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\nEREREZEM2FgTEREREcng/wCRlSUNit2IowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa729e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_variable_importance(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_rfc = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_gbc = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_kn = KNeighborsClassifier(n_neighbors = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_lg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lg.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.789727126806 0.80223880597\n"
     ]
    }
   ],
   "source": [
    "print(model_lg.score( train_X, train_y), model_lg.score(valid_X, valid_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.987158908507\n"
     ]
    },
    {
     "data": {
      "image/png": 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UoSOjfV0GgOsAwbo3Ojp05sl0X1cBAPgShawr9nUJAK4TtIIAAAAABhCsAQAAAAMI1gAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa86d830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_var_imp(model, train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=sklearn.cross_validation.StratifiedKFold(labels=[ 1.  0. ...,  0.  1.], n_folds=2, shuffle=False, random_state=None),\n",
       "   estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "   estimator_params=None, scoring='accuracy', step=1, verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv = RFECV(estimator = model, step = 1, cv = StratifiedKFold(train_y, 2), scoring = 'accuracy')\n",
    "rfecv.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_Y = model.predict( test_X )\n",
    "passenger_id = full[891:].PassengerId\n",
    "test = pd.DataFrame( {'PassengerId': passenger_id, 'Survived': test_Y })\n",
    "test.shape\n",
    "test.head()\n",
    "test.to_csv('titanic_pred.csv' , index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
